/**
* This file is part of ORB-SLAM2.
* This file is based on the file orb.cpp from the OpenCV library (see BSD
* license below).
*
* Copyright (C) 2014-2016 Raúl Mur-Artal <raulmur at unizar dot es> (University
* of Zaragoza)
* For more information see <https://github.com/raulmur/ORB_SLAM2>
*
* ORB-SLAM2 is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* ORB-SLAM2 is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with ORB-SLAM2. If not, see <http://www.gnu.org/licenses/>.
*/
/**
* Software License Agreement (BSD License)
*
*  Copyright (c) 2009, Willow Garage, Inc.
*  All rights reserved.
*
*  Redistribution and use in source and binary forms, with or without
*  modification, are permitted provided that the following conditions
*  are met:
*
*   * Redistributions of source code must retain the above copyright
*     notice, this list of conditions and the following disclaimer.
*   * Redistributions in binary form must reproduce the above
*     copyright notice, this list of conditions and the following
*     disclaimer in the documentation and/or other materials provided
*     with the distribution.
*   * Neither the name of the Willow Garage nor the names of its
*     contributors may be used to endorse or promote products derived
*     from this software without specific prior written permission.
*
*  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
*  "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
*  LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
*  FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
*  COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
*  INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
*  BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
*  LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
*  CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
*  LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
*  ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
*  POSSIBILITY OF SUCH DAMAGE.
*
*/

#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/features2d/features2d.hpp>
#include <opencv2/imgproc/imgproc.hpp>

#include <vector>
#include <utility>

#include "include/orbslam/ORBextractor.h"

namespace SIVO {

const int PATCH_SIZE = 31;
const int HALF_PATCH_SIZE = 15;
const int EDGE_THRESHOLD = 19;


static float IC_Angle(const cv::Mat &image, cv::Point2f pt, const std::vector<int> &u_max) {
    int m_01 = 0, m_10 = 0;

    const uchar *center = &image.at<uchar>(cvRound(pt.y), cvRound(pt.x));

    // Treat the center line differently, v=0
    for (int u = -HALF_PATCH_SIZE; u <= HALF_PATCH_SIZE; ++u)
        m_10 += u * center[u];

    // Go line by line in the circuI853lar patch
    int step = (int) image.step1();
    for (int v = 1; v <= HALF_PATCH_SIZE; ++v) {
        // Proceed over the two lines
        int v_sum = 0;
        int d = u_max[v];
        for (int u = -d; u <= d; ++u) {
            int val_plus = center[u + v * step],
                val_minus = center[u - v * step];
            v_sum += (val_plus - val_minus);
            m_10 += u * (val_plus + val_minus);
        }
        m_01 += v * v_sum;
    }

    return cv::fastAtan2((float) m_01, (float) m_10);
}


const float factorPI = (float) (CV_PI / 180.f);
static void computeOrbDescriptor(const cv::KeyPoint &kpt,
                                 const cv::Mat &img,
                                 const cv::Point *pattern,
                                 uchar *desc) {
    float angle = (float) kpt.angle * factorPI;
    float a = (float) cos(angle), b = (float) sin(angle);

    const uchar *center = &img.at<uchar>(cvRound(kpt.pt.y), cvRound(kpt.pt.x));
    const int step = (int) img.step;

#define GET_VALUE(idx)                                               \
    center[cvRound(pattern[idx].x * b + pattern[idx].y * a) * step + \
           cvRound(pattern[idx].x * a - pattern[idx].y * b)]


    for (int i = 0; i < 32; ++i, pattern += 16) {
        int t0, t1, val;
        t0 = GET_VALUE(0);
        t1 = GET_VALUE(1);
        val = t0 < t1;
        t0 = GET_VALUE(2);
        t1 = GET_VALUE(3);
        val |= (t0 < t1) << 1;
        t0 = GET_VALUE(4);
        t1 = GET_VALUE(5);
        val |= (t0 < t1) << 2;
        t0 = GET_VALUE(6);
        t1 = GET_VALUE(7);
        val |= (t0 < t1) << 3;
        t0 = GET_VALUE(8);
        t1 = GET_VALUE(9);
        val |= (t0 < t1) << 4;
        t0 = GET_VALUE(10);
        t1 = GET_VALUE(11);
        val |= (t0 < t1) << 5;
        t0 = GET_VALUE(12);
        t1 = GET_VALUE(13);
        val |= (t0 < t1) << 6;
        t0 = GET_VALUE(14);
        t1 = GET_VALUE(15);
        val |= (t0 < t1) << 7;

        desc[i] = (uchar) val;
    }

#undef GET_VALUE
}


static int bit_pattern_31_[256 * 4] = {
  8,   -3,  9,   5 /*mean (0), correlation (0)*/,
  4,   2,   7,   -12 /*mean (1.12461e-05), correlation (0.0437584)*/,
  -11, 9,   -8,  2 /*mean (3.37382e-05), correlation (0.0617409)*/,
  7,   -12, 12,  -13 /*mean (5.62303e-05), correlation (0.0636977)*/,
  2,   -13, 2,   12 /*mean (0.000134953), correlation (0.085099)*/,
  1,   -7,  1,   6 /*mean (0.000528565), correlation (0.0857175)*/,
  -2,  -10, -2,  -4 /*mean (0.0188821), correlation (0.0985774)*/,
  -13, -13, -11, -8 /*mean (0.0363135), correlation (0.0899616)*/,
  -13, -3,  -12, -9 /*mean (0.121806), correlation (0.099849)*/,
  10,  4,   11,  9 /*mean (0.122065), correlation (0.093285)*/,
  -13, -8,  -8,  -9 /*mean (0.162787), correlation (0.0942748)*/,
  -11, 7,   -9,  12 /*mean (0.21561), correlation (0.0974438)*/,
  7,   7,   12,  6 /*mean (0.160583), correlation (0.130064)*/,
  -4,  -5,  -3,  0 /*mean (0.228171), correlation (0.132998)*/,
  -13, 2,   -12, -3 /*mean (0.00997526), correlation (0.145926)*/,
  -9,  0,   -7,  5 /*mean (0.198234), correlation (0.143636)*/,
  12,  -6,  12,  -1 /*mean (0.0676226), correlation (0.16689)*/,
  -3,  6,   -2,  12 /*mean (0.166847), correlation (0.171682)*/,
  -6,  -13, -4,  -8 /*mean (0.101215), correlation (0.179716)*/,
  11,  -13, 12,  -8 /*mean (0.200641), correlation (0.192279)*/,
  4,   7,   5,   1 /*mean (0.205106), correlation (0.186848)*/,
  5,   -3,  10,  -3 /*mean (0.234908), correlation (0.192319)*/,
  3,   -7,  6,   12 /*mean (0.0709964), correlation (0.210872)*/,
  -8,  -7,  -6,  -2 /*mean (0.0939834), correlation (0.212589)*/,
  -2,  11,  -1,  -10 /*mean (0.127778), correlation (0.20866)*/,
  -13, 12,  -8,  10 /*mean (0.14783), correlation (0.206356)*/,
  -7,  3,   -5,  -3 /*mean (0.182141), correlation (0.198942)*/,
  -4,  2,   -3,  7 /*mean (0.188237), correlation (0.21384)*/,
  -10, -12, -6,  11 /*mean (0.14865), correlation (0.23571)*/,
  5,   -12, 6,   -7 /*mean (0.222312), correlation (0.23324)*/,
  5,   -6,  7,   -1 /*mean (0.229082), correlation (0.23389)*/,
  1,   0,   4,   -5 /*mean (0.241577), correlation (0.215286)*/,
  9,   11,  11,  -13 /*mean (0.00338507), correlation (0.251373)*/,
  4,   7,   4,   12 /*mean (0.131005), correlation (0.257622)*/,
  2,   -1,  4,   4 /*mean (0.152755), correlation (0.255205)*/,
  -4,  -12, -2,  7 /*mean (0.182771), correlation (0.244867)*/,
  -8,  -5,  -7,  -10 /*mean (0.186898), correlation (0.23901)*/,
  4,   11,  9,   12 /*mean (0.226226), correlation (0.258255)*/,
  0,   -8,  1,   -13 /*mean (0.0897886), correlation (0.274827)*/,
  -13, -2,  -8,  2 /*mean (0.148774), correlation (0.28065)*/,
  -3,  -2,  -2,  3 /*mean (0.153048), correlation (0.283063)*/,
  -6,  9,   -4,  -9 /*mean (0.169523), correlation (0.278248)*/,
  8,   12,  10,  7 /*mean (0.225337), correlation (0.282851)*/,
  0,   9,   1,   3 /*mean (0.226687), correlation (0.278734)*/,
  7,   -5,  11,  -10 /*mean (0.00693882), correlation (0.305161)*/,
  -13, -6,  -11, 0 /*mean (0.0227283), correlation (0.300181)*/,
  10,  7,   12,  1 /*mean (0.125517), correlation (0.31089)*/,
  -6,  -3,  -6,  12 /*mean (0.131748), correlation (0.312779)*/,
  10,  -9,  12,  -4 /*mean (0.144827), correlation (0.292797)*/,
  -13, 8,   -8,  -12 /*mean (0.149202), correlation (0.308918)*/,
  -13, 0,   -8,  -4 /*mean (0.160909), correlation (0.310013)*/,
  3,   3,   7,   8 /*mean (0.177755), correlation (0.309394)*/,
  5,   7,   10,  -7 /*mean (0.212337), correlation (0.310315)*/,
  -1,  7,   1,   -12 /*mean (0.214429), correlation (0.311933)*/,
  3,   -10, 5,   6 /*mean (0.235807), correlation (0.313104)*/,
  2,   -4,  3,   -10 /*mean (0.00494827), correlation (0.344948)*/,
  -13, 0,   -13, 5 /*mean (0.0549145), correlation (0.344675)*/,
  -13, -7,  -12, 12 /*mean (0.103385), correlation (0.342715)*/,
  -13, 3,   -11, 8 /*mean (0.134222), correlation (0.322922)*/,
  -7,  12,  -4,  7 /*mean (0.153284), correlation (0.337061)*/,
  6,   -10, 12,  8 /*mean (0.154881), correlation (0.329257)*/,
  -9,  -1,  -7,  -6 /*mean (0.200967), correlation (0.33312)*/,
  -2,  -5,  0,   12 /*mean (0.201518), correlation (0.340635)*/,
  -12, 5,   -7,  5 /*mean (0.207805), correlation (0.335631)*/,
  3,   -10, 8,   -13 /*mean (0.224438), correlation (0.34504)*/,
  -7,  -7,  -4,  5 /*mean (0.239361), correlation (0.338053)*/,
  -3,  -2,  -1,  -7 /*mean (0.240744), correlation (0.344322)*/,
  2,   9,   5,   -11 /*mean (0.242949), correlation (0.34145)*/,
  -11, -13, -5,  -13 /*mean (0.244028), correlation (0.336861)*/,
  -1,  6,   0,   -1 /*mean (0.247571), correlation (0.343684)*/,
  5,   -3,  5,   2 /*mean (0.000697256), correlation (0.357265)*/,
  -4,  -13, -4,  12 /*mean (0.00213675), correlation (0.373827)*/,
  -9,  -6,  -9,  6 /*mean (0.0126856), correlation (0.373938)*/,
  -12, -10, -8,  -4 /*mean (0.0152497), correlation (0.364237)*/,
  10,  2,   12,  -3 /*mean (0.0299933), correlation (0.345292)*/,
  7,   12,  12,  12 /*mean (0.0307242), correlation (0.366299)*/,
  -7,  -13, -6,  5 /*mean (0.0534975), correlation (0.368357)*/,
  -4,  9,   -3,  4 /*mean (0.099865), correlation (0.372276)*/,
  7,   -1,  12,  2 /*mean (0.117083), correlation (0.364529)*/,
  -7,  6,   -5,  1 /*mean (0.126125), correlation (0.369606)*/,
  -13, 11,  -12, 5 /*mean (0.130364), correlation (0.358502)*/,
  -3,  7,   -2,  -6 /*mean (0.131691), correlation (0.375531)*/,
  7,   -8,  12,  -7 /*mean (0.160166), correlation (0.379508)*/,
  -13, -7,  -11, -12 /*mean (0.167848), correlation (0.353343)*/,
  1,   -3,  12,  12 /*mean (0.183378), correlation (0.371916)*/,
  2,   -6,  3,   0 /*mean (0.228711), correlation (0.371761)*/,
  -4,  3,   -2,  -13 /*mean (0.247211), correlation (0.364063)*/,
  -1,  -13, 1,   9 /*mean (0.249325), correlation (0.378139)*/,
  7,   1,   8,   -6 /*mean (0.000652272), correlation (0.411682)*/,
  1,   -1,  3,   12 /*mean (0.00248538), correlation (0.392988)*/,
  9,   1,   12,  6 /*mean (0.0206815), correlation (0.386106)*/,
  -1,  -9,  -1,  3 /*mean (0.0364485), correlation (0.410752)*/,
  -13, -13, -10, 5 /*mean (0.0376068), correlation (0.398374)*/,
  7,   7,   10,  12 /*mean (0.0424202), correlation (0.405663)*/,
  12,  -5,  12,  9 /*mean (0.0942645), correlation (0.410422)*/,
  6,   3,   7,   11 /*mean (0.1074), correlation (0.413224)*/,
  5,   -13, 6,   10 /*mean (0.109256), correlation (0.408646)*/,
  2,   -12, 2,   3 /*mean (0.131691), correlation (0.416076)*/,
  3,   8,   4,   -6 /*mean (0.165081), correlation (0.417569)*/,
  2,   6,   12,  -13 /*mean (0.171874), correlation (0.408471)*/,
  9,   -12, 10,  3 /*mean (0.175146), correlation (0.41296)*/,
  -8,  4,   -7,  9 /*mean (0.183682), correlation (0.402956)*/,
  -11, 12,  -4,  -6 /*mean (0.184672), correlation (0.416125)*/,
  1,   12,  2,   -8 /*mean (0.191487), correlation (0.386696)*/,
  6,   -9,  7,   -4 /*mean (0.192668), correlation (0.394771)*/,
  2,   3,   3,   -2 /*mean (0.200157), correlation (0.408303)*/,
  6,   3,   11,  0 /*mean (0.204588), correlation (0.411762)*/,
  3,   -3,  8,   -8 /*mean (0.205904), correlation (0.416294)*/,
  7,   8,   9,   3 /*mean (0.213237), correlation (0.409306)*/,
  -11, -5,  -6,  -4 /*mean (0.243444), correlation (0.395069)*/,
  -10, 11,  -5,  10 /*mean (0.247672), correlation (0.413392)*/,
  -5,  -8,  -3,  12 /*mean (0.24774), correlation (0.411416)*/,
  -10, 5,   -9,  0 /*mean (0.00213675), correlation (0.454003)*/,
  8,   -1,  12,  -6 /*mean (0.0293635), correlation (0.455368)*/,
  4,   -6,  6,   -11 /*mean (0.0404971), correlation (0.457393)*/,
  -10, 12,  -8,  7 /*mean (0.0481107), correlation (0.448364)*/,
  4,   -2,  6,   7 /*mean (0.050641), correlation (0.455019)*/,
  -2,  0,   -2,  12 /*mean (0.0525978), correlation (0.44338)*/,
  -5,  -8,  -5,  2 /*mean (0.0629667), correlation (0.457096)*/,
  7,   -6,  10,  12 /*mean (0.0653846), correlation (0.445623)*/,
  -9,  -13, -8,  -8 /*mean (0.0858749), correlation (0.449789)*/,
  -5,  -13, -5,  -2 /*mean (0.122402), correlation (0.450201)*/,
  8,   -8,  9,   -13 /*mean (0.125416), correlation (0.453224)*/,
  -9,  -11, -9,  0 /*mean (0.130128), correlation (0.458724)*/,
  1,   -8,  1,   -2 /*mean (0.132467), correlation (0.440133)*/,
  7,   -4,  9,   1 /*mean (0.132692), correlation (0.454)*/,
  -2,  1,   -1,  -4 /*mean (0.135695), correlation (0.455739)*/,
  11,  -6,  12,  -11 /*mean (0.142904), correlation (0.446114)*/,
  -12, -9,  -6,  4 /*mean (0.146165), correlation (0.451473)*/,
  3,   7,   7,   12 /*mean (0.147627), correlation (0.456643)*/,
  5,   5,   10,  8 /*mean (0.152901), correlation (0.455036)*/,
  0,   -4,  2,   8 /*mean (0.167083), correlation (0.459315)*/,
  -9,  12,  -5,  -13 /*mean (0.173234), correlation (0.454706)*/,
  0,   7,   2,   12 /*mean (0.18312), correlation (0.433855)*/,
  -1,  2,   1,   7 /*mean (0.185504), correlation (0.443838)*/,
  5,   11,  7,   -9 /*mean (0.185706), correlation (0.451123)*/,
  3,   5,   6,   -8 /*mean (0.188968), correlation (0.455808)*/,
  -13, -4,  -8,  9 /*mean (0.191667), correlation (0.459128)*/,
  -5,  9,   -3,  -3 /*mean (0.193196), correlation (0.458364)*/,
  -4,  -7,  -3,  -12 /*mean (0.196536), correlation (0.455782)*/,
  6,   5,   8,   0 /*mean (0.1972), correlation (0.450481)*/,
  -7,  6,   -6,  12 /*mean (0.199438), correlation (0.458156)*/,
  -13, 6,   -5,  -2 /*mean (0.211224), correlation (0.449548)*/,
  1,   -10, 3,   10 /*mean (0.211718), correlation (0.440606)*/,
  4,   1,   8,   -4 /*mean (0.213034), correlation (0.443177)*/,
  -2,  -2,  2,   -13 /*mean (0.234334), correlation (0.455304)*/,
  2,   -12, 12,  12 /*mean (0.235684), correlation (0.443436)*/,
  -2,  -13, 0,   -6 /*mean (0.237674), correlation (0.452525)*/,
  4,   1,   9,   3 /*mean (0.23962), correlation (0.444824)*/,
  -6,  -10, -3,  -5 /*mean (0.248459), correlation (0.439621)*/,
  -3,  -13, -1,  1 /*mean (0.249505), correlation (0.456666)*/,
  7,   5,   12,  -11 /*mean (0.00119208), correlation (0.495466)*/,
  4,   -2,  5,   -7 /*mean (0.00372245), correlation (0.484214)*/,
  -13, 9,   -9,  -5 /*mean (0.00741116), correlation (0.499854)*/,
  7,   1,   8,   6 /*mean (0.0208952), correlation (0.499773)*/,
  7,   -8,  7,   6 /*mean (0.0220085), correlation (0.501609)*/,
  -7,  -4,  -7,  1 /*mean (0.0233806), correlation (0.496568)*/,
  -8,  11,  -7,  -8 /*mean (0.0236505), correlation (0.489719)*/,
  -13, 6,   -12, -8 /*mean (0.0268781), correlation (0.503487)*/,
  2,   4,   3,   9 /*mean (0.0323324), correlation (0.501938)*/,
  10,  -5,  12,  3 /*mean (0.0399235), correlation (0.494029)*/,
  -6,  -5,  -6,  7 /*mean (0.0420153), correlation (0.486579)*/,
  8,   -3,  9,   -8 /*mean (0.0548021), correlation (0.484237)*/,
  2,   -12, 2,   8 /*mean (0.0616622), correlation (0.496642)*/,
  -11, -2,  -10, 3 /*mean (0.0627755), correlation (0.498563)*/,
  -12, -13, -7,  -9 /*mean (0.0829622), correlation (0.495491)*/,
  -11, 0,   -10, -5 /*mean (0.0843342), correlation (0.487146)*/,
  5,   -3,  11,  8 /*mean (0.0929937), correlation (0.502315)*/,
  -2,  -13, -1,  12 /*mean (0.113327), correlation (0.48941)*/,
  -1,  -8,  0,   9 /*mean (0.132119), correlation (0.467268)*/,
  -13, -11, -12, -5 /*mean (0.136269), correlation (0.498771)*/,
  -10, -2,  -10, 11 /*mean (0.142173), correlation (0.498714)*/,
  -3,  9,   -2,  -13 /*mean (0.144141), correlation (0.491973)*/,
  2,   -3,  3,   2 /*mean (0.14892), correlation (0.500782)*/,
  -9,  -13, -4,  0 /*mean (0.150371), correlation (0.498211)*/,
  -4,  6,   -3,  -10 /*mean (0.152159), correlation (0.495547)*/,
  -4,  12,  -2,  -7 /*mean (0.156152), correlation (0.496925)*/,
  -6,  -11, -4,  9 /*mean (0.15749), correlation (0.499222)*/,
  6,   -3,  6,   11 /*mean (0.159211), correlation (0.503821)*/,
  -13, 11,  -5,  5 /*mean (0.162427), correlation (0.501907)*/,
  11,  11,  12,  6 /*mean (0.16652), correlation (0.497632)*/,
  7,   -5,  12,  -2 /*mean (0.169141), correlation (0.484474)*/,
  -1,  12,  0,   7 /*mean (0.169456), correlation (0.495339)*/,
  -4,  -8,  -3,  -2 /*mean (0.171457), correlation (0.487251)*/,
  -7,  1,   -6,  7 /*mean (0.175), correlation (0.500024)*/,
  -13, -12, -8,  -13 /*mean (0.175866), correlation (0.497523)*/,
  -7,  -2,  -6,  -8 /*mean (0.178273), correlation (0.501854)*/,
  -8,  5,   -6,  -9 /*mean (0.181107), correlation (0.494888)*/,
  -5,  -1,  -4,  5 /*mean (0.190227), correlation (0.482557)*/,
  -13, 7,   -8,  10 /*mean (0.196739), correlation (0.496503)*/,
  1,   5,   5,   -13 /*mean (0.19973), correlation (0.499759)*/,
  1,   0,   10,  -13 /*mean (0.204465), correlation (0.49873)*/,
  9,   12,  10,  -1 /*mean (0.209334), correlation (0.49063)*/,
  5,   -8,  10,  -9 /*mean (0.211134), correlation (0.503011)*/,
  -1,  11,  1,   -13 /*mean (0.212), correlation (0.499414)*/,
  -9,  -3,  -6,  2 /*mean (0.212168), correlation (0.480739)*/,
  -1,  -10, 1,   12 /*mean (0.212731), correlation (0.502523)*/,
  -13, 1,   -8,  -10 /*mean (0.21327), correlation (0.489786)*/,
  8,   -11, 10,  -6 /*mean (0.214159), correlation (0.488246)*/,
  2,   -13, 3,   -6 /*mean (0.216993), correlation (0.50287)*/,
  7,   -13, 12,  -9 /*mean (0.223639), correlation (0.470502)*/,
  -10, -10, -5,  -7 /*mean (0.224089), correlation (0.500852)*/,
  -10, -8,  -8,  -13 /*mean (0.228666), correlation (0.502629)*/,
  4,   -6,  8,   5 /*mean (0.22906), correlation (0.498305)*/,
  3,   12,  8,   -13 /*mean (0.233378), correlation (0.503825)*/,
  -4,  2,   -3,  -3 /*mean (0.234323), correlation (0.476692)*/,
  5,   -13, 10,  -12 /*mean (0.236392), correlation (0.475462)*/,
  4,   -13, 5,   -1 /*mean (0.236842), correlation (0.504132)*/,
  -9,  9,   -4,  3 /*mean (0.236977), correlation (0.497739)*/,
  0,   3,   3,   -9 /*mean (0.24314), correlation (0.499398)*/,
  -12, 1,   -6,  1 /*mean (0.243297), correlation (0.489447)*/,
  3,   2,   4,   -8 /*mean (0.00155196), correlation (0.553496)*/,
  -10, -10, -10, 9 /*mean (0.00239541), correlation (0.54297)*/,
  8,   -13, 12,  12 /*mean (0.0034413), correlation (0.544361)*/,
  -8,  -12, -6,  -5 /*mean (0.003565), correlation (0.551225)*/,
  2,   2,   3,   7 /*mean (0.00835583), correlation (0.55285)*/,
  10,  6,   11,  -8 /*mean (0.00885065), correlation (0.540913)*/,
  6,   8,   8,   -12 /*mean (0.0101552), correlation (0.551085)*/,
  -7,  10,  -6,  5 /*mean (0.0102227), correlation (0.533635)*/,
  -3,  -9,  -3,  9 /*mean (0.0110211), correlation (0.543121)*/,
  -1,  -13, -1,  5 /*mean (0.0113473), correlation (0.550173)*/,
  -3,  -7,  -3,  4 /*mean (0.0140913), correlation (0.554774)*/,
  -8,  -2,  -8,  3 /*mean (0.017049), correlation (0.55461)*/,
  4,   2,   12,  12 /*mean (0.01778), correlation (0.546921)*/,
  2,   -5,  3,   11 /*mean (0.0224022), correlation (0.549667)*/,
  6,   -9,  11,  -13 /*mean (0.029161), correlation (0.546295)*/,
  3,   -1,  7,   12 /*mean (0.0303081), correlation (0.548599)*/,
  11,  -1,  12,  4 /*mean (0.0355151), correlation (0.523943)*/,
  -3,  0,   -3,  6 /*mean (0.0417904), correlation (0.543395)*/,
  4,   -11, 4,   12 /*mean (0.0487292), correlation (0.542818)*/,
  2,   -4,  2,   1 /*mean (0.0575124), correlation (0.554888)*/,
  -10, -6,  -8,  1 /*mean (0.0594242), correlation (0.544026)*/,
  -13, 7,   -11, 1 /*mean (0.0597391), correlation (0.550524)*/,
  -13, 12,  -11, -13 /*mean (0.0608974), correlation (0.55383)*/,
  6,   0,   11,  -13 /*mean (0.065126), correlation (0.552006)*/,
  0,   -1,  1,   4 /*mean (0.074224), correlation (0.546372)*/,
  -13, 3,   -9,  -2 /*mean (0.0808592), correlation (0.554875)*/,
  -9,  8,   -6,  -3 /*mean (0.0883378), correlation (0.551178)*/,
  -13, -6,  -8,  -2 /*mean (0.0901035), correlation (0.548446)*/,
  5,   -9,  8,   10 /*mean (0.0949843), correlation (0.554694)*/,
  2,   7,   3,   -9 /*mean (0.0994152), correlation (0.550979)*/,
  -1,  -6,  -1,  -1 /*mean (0.10045), correlation (0.552714)*/,
  9,   5,   11,  -2 /*mean (0.100686), correlation (0.552594)*/,
  11,  -3,  12,  -8 /*mean (0.101091), correlation (0.532394)*/,
  3,   0,   3,   5 /*mean (0.101147), correlation (0.525576)*/,
  -1,  4,   0,   10 /*mean (0.105263), correlation (0.531498)*/,
  3,   -6,  4,   5 /*mean (0.110785), correlation (0.540491)*/,
  -13, 0,   -10, 5 /*mean (0.112798), correlation (0.536582)*/,
  5,   8,   12,  11 /*mean (0.114181), correlation (0.555793)*/,
  8,   9,   9,   -6 /*mean (0.117431), correlation (0.553763)*/,
  7,   -4,  8,   -12 /*mean (0.118522), correlation (0.553452)*/,
  -10, 4,   -10, 9 /*mean (0.12094), correlation (0.554785)*/,
  7,   3,   12,  4 /*mean (0.122582), correlation (0.555825)*/,
  9,   -7,  10,  -2 /*mean (0.124978), correlation (0.549846)*/,
  7,   0,   12,  -2 /*mean (0.127002), correlation (0.537452)*/,
  -1,  -6,  0,   -11 /*mean (0.127148), correlation (0.547401)*/
};

ORBextractor::ORBextractor(int _nfeatures,
                           float _scaleFactor,
                           int _nlevels,
                           int _iniThFAST,
                           int _minThFAST)
    : nfeatures(_nfeatures),
      scaleFactor(_scaleFactor),
      nlevels(_nlevels),
      iniThFAST(_iniThFAST),
      minThFAST(_minThFAST) {
    mvScaleFactor.resize(nlevels);
    mvLevelSigma2.resize(nlevels);
    mvScaleFactor[0] = 1.0f;
    mvLevelSigma2[0] = 1.0f;
    for (int i = 1; i < nlevels; i++) {
        mvScaleFactor[i] = mvScaleFactor[i - 1] * scaleFactor;
        mvLevelSigma2[i] = mvScaleFactor[i] * mvScaleFactor[i];
    }

    mvInvScaleFactor.resize(nlevels);
    mvInvLevelSigma2.resize(nlevels);
    for (int i = 0; i < nlevels; i++) {
        mvInvScaleFactor[i] = 1.0f / mvScaleFactor[i];
        mvInvLevelSigma2[i] = 1.0f / mvLevelSigma2[i];
    }

    mvImagePyramid.resize(nlevels);

    mnFeaturesPerLevel.resize(nlevels);
    float factor = 1.0f / scaleFactor;
    float nDesiredFeaturesPerScale =
      nfeatures * (1 - factor) /
      (1 - (float) pow((double) factor, (double) nlevels));

    int sumFeatures = 0;
    for (int level = 0; level < nlevels - 1; level++) {
        mnFeaturesPerLevel[level] = cvRound(nDesiredFeaturesPerScale);
        sumFeatures += mnFeaturesPerLevel[level];
        nDesiredFeaturesPerScale *= factor;
    }
    mnFeaturesPerLevel[nlevels - 1] = std::max(nfeatures - sumFeatures, 0);

    const int npoints = 512;
    const cv::Point *pattern0 = (const cv::Point *) bit_pattern_31_;
    std::copy(pattern0, pattern0 + npoints, std::back_inserter(pattern));

    // This is for orientation
    // pre-compute the end of a row in a circular patch
    umax.resize(HALF_PATCH_SIZE + 1);

    int v, v0, vmax = cvFloor(HALF_PATCH_SIZE * sqrt(2.f) / 2 + 1);
    int vmin = cvCeil(HALF_PATCH_SIZE * sqrt(2.f) / 2);
    const double hp2 = HALF_PATCH_SIZE * HALF_PATCH_SIZE;
    for (v = 0; v <= vmax; ++v)
        umax[v] = cvRound(sqrt(hp2 - v * v));

    // Make sure we are symmetric
    for (v = HALF_PATCH_SIZE, v0 = 0; v >= vmin; --v) {
        while (umax[v0] == umax[v0 + 1])
            ++v0;
        umax[v] = v0;
        ++v0;
    }
}

static void computeOrientation(const cv::Mat &image,
                               std::vector<cv::KeyPoint> &keypoints,
                               const std::vector<int> &umax) {
    for (std::vector<cv::KeyPoint>::iterator keypoint = keypoints.begin(),
                                    keypointEnd = keypoints.end();
         keypoint != keypointEnd;
         ++keypoint) {
        keypoint->angle = IC_Angle(image, keypoint->pt, umax);
    }
}

void ExtractorNode::DivideNode(ExtractorNode &n1,
                               ExtractorNode &n2,
                               ExtractorNode &n3,
                               ExtractorNode &n4) {
    const int halfX = ceil(static_cast<float>(UR.x - UL.x) / 2);
    const int halfY = ceil(static_cast<float>(BR.y - UL.y) / 2);

    // Define boundaries of childs
    n1.UL = UL;
    n1.UR = cv::Point2i(UL.x + halfX, UL.y);
    n1.BL = cv::Point2i(UL.x, UL.y + halfY);
    n1.BR = cv::Point2i(UL.x + halfX, UL.y + halfY);
    n1.vKeys.reserve(vKeys.size());

    n2.UL = n1.UR;
    n2.UR = UR;
    n2.BL = n1.BR;
    n2.BR = cv::Point2i(UR.x, UL.y + halfY);
    n2.vKeys.reserve(vKeys.size());

    n3.UL = n1.BL;
    n3.UR = n1.BR;
    n3.BL = BL;
    n3.BR = cv::Point2i(n1.BR.x, BL.y);
    n3.vKeys.reserve(vKeys.size());

    n4.UL = n3.UR;
    n4.UR = n2.BR;
    n4.BL = n3.BR;
    n4.BR = BR;
    n4.vKeys.reserve(vKeys.size());

    // Associate points to childs
    for (size_t i = 0; i < vKeys.size(); i++) {
        const cv::KeyPoint &kp = vKeys[i];
        if (kp.pt.x < n1.UR.x) {
            if (kp.pt.y < n1.BR.y)
                n1.vKeys.push_back(kp);
            else
                n3.vKeys.push_back(kp);
        } else if (kp.pt.y < n1.BR.y)
            n2.vKeys.push_back(kp);
        else
            n4.vKeys.push_back(kp);
    }

    if (n1.vKeys.size() == 1)
        n1.bNoMore = true;
    if (n2.vKeys.size() == 1)
        n2.bNoMore = true;
    if (n3.vKeys.size() == 1)
        n3.bNoMore = true;
    if (n4.vKeys.size() == 1)
        n4.bNoMore = true;
}

std::vector<cv::KeyPoint> ORBextractor::DistributeOctTree(
  const std::vector<cv::KeyPoint> &vToDistributeKeys,
  const int &minX,
  const int &maxX,
  const int &minY,
  const int &maxY,
  const int &N,
  const int &level) {
    // Compute how many initial nodes
    const int nIni = round(static_cast<float>(maxX - minX) / (maxY - minY));

    const float hX = static_cast<float>(maxX - minX) / nIni;

    std::list<ExtractorNode> lNodes;

    std::vector<ExtractorNode *> vpIniNodes;
    vpIniNodes.resize(nIni);

    for (int i = 0; i < nIni; i++) {
        ExtractorNode ni;
        ni.UL = cv::Point2i(hX * static_cast<float>(i), 0);
        ni.UR = cv::Point2i(hX * static_cast<float>(i + 1), 0);
        ni.BL = cv::Point2i(ni.UL.x, maxY - minY);
        ni.BR = cv::Point2i(ni.UR.x, maxY - minY);
        ni.vKeys.reserve(vToDistributeKeys.size());

        lNodes.push_back(ni);
        vpIniNodes[i] = &lNodes.back();
    }

    // Associate points to childs
    for (size_t i = 0; i < vToDistributeKeys.size(); i++) {
        const cv::KeyPoint &kp = vToDistributeKeys[i];
        vpIniNodes[kp.pt.x / hX]->vKeys.push_back(kp);
    }

    std::list<ExtractorNode>::iterator lit = lNodes.begin();

    while (lit != lNodes.end()) {
        if (lit->vKeys.size() == 1) {
            lit->bNoMore = true;
            lit++;
        } else if (lit->vKeys.empty())
            lit = lNodes.erase(lit);
        else
            lit++;
    }

    bool bFinish = false;

    int iteration = 0;

    std::vector<std::pair<int, ExtractorNode *> > vSizeAndPointerToNode;
    vSizeAndPointerToNode.reserve(lNodes.size() * 4);

    while (!bFinish) {
        iteration++;

        int prevSize = lNodes.size();

        lit = lNodes.begin();

        int nToExpand = 0;

        vSizeAndPointerToNode.clear();

        while (lit != lNodes.end()) {
            if (lit->bNoMore) {
                // If node only contains one point do not subdivide and continue
                lit++;
                continue;
            } else {
                // If more than one point, subdivide
                ExtractorNode n1, n2, n3, n4;
                lit->DivideNode(n1, n2, n3, n4);

                // Add childs if they contain points
                if (n1.vKeys.size() > 0) {
                    lNodes.push_front(n1);
                    if (n1.vKeys.size() > 1) {
                        nToExpand++;
                        vSizeAndPointerToNode.push_back(
                          std::make_pair(n1.vKeys.size(), &lNodes.front()));
                        lNodes.front().lit = lNodes.begin();
                    }
                }
                if (n2.vKeys.size() > 0) {
                    lNodes.push_front(n2);
                    if (n2.vKeys.size() > 1) {
                        nToExpand++;
                        vSizeAndPointerToNode.push_back(
                          std::make_pair(n2.vKeys.size(), &lNodes.front()));
                        lNodes.front().lit = lNodes.begin();
                    }
                }
                if (n3.vKeys.size() > 0) {
                    lNodes.push_front(n3);
                    if (n3.vKeys.size() > 1) {
                        nToExpand++;
                        vSizeAndPointerToNode.push_back(
                          std::make_pair(n3.vKeys.size(), &lNodes.front()));
                        lNodes.front().lit = lNodes.begin();
                    }
                }
                if (n4.vKeys.size() > 0) {
                    lNodes.push_front(n4);
                    if (n4.vKeys.size() > 1) {
                        nToExpand++;
                        vSizeAndPointerToNode.push_back(
                          std::make_pair(n4.vKeys.size(), &lNodes.front()));
                        lNodes.front().lit = lNodes.begin();
                    }
                }

                lit = lNodes.erase(lit);
                continue;
            }
        }

        // Finish if there are more nodes than required features
        // or all nodes contain just one point
        if ((int) lNodes.size() >= N || (int) lNodes.size() == prevSize) {
            bFinish = true;
        } else if (((int) lNodes.size() + nToExpand * 3) > N) {
            while (!bFinish) {
                prevSize = lNodes.size();

                std::vector<std::pair<int, ExtractorNode *> > vPrevSizeAndPointerToNode =
                  vSizeAndPointerToNode;
                vSizeAndPointerToNode.clear();

                std::sort(vPrevSizeAndPointerToNode.begin(),
                     vPrevSizeAndPointerToNode.end());
                for (int j = vPrevSizeAndPointerToNode.size() - 1; j >= 0;
                     j--) {
                    ExtractorNode n1, n2, n3, n4;
                    vPrevSizeAndPointerToNode[j].second->DivideNode(
                      n1, n2, n3, n4);

                    // Add childs if they contain points
                    if (n1.vKeys.size() > 0) {
                        lNodes.push_front(n1);
                        if (n1.vKeys.size() > 1) {
                            vSizeAndPointerToNode.push_back(
                              std::make_pair(n1.vKeys.size(), &lNodes.front()));
                            lNodes.front().lit = lNodes.begin();
                        }
                    }
                    if (n2.vKeys.size() > 0) {
                        lNodes.push_front(n2);
                        if (n2.vKeys.size() > 1) {
                            vSizeAndPointerToNode.push_back(
                              std::make_pair(n2.vKeys.size(), &lNodes.front()));
                            lNodes.front().lit = lNodes.begin();
                        }
                    }
                    if (n3.vKeys.size() > 0) {
                        lNodes.push_front(n3);
                        if (n3.vKeys.size() > 1) {
                            vSizeAndPointerToNode.push_back(
                              std::make_pair(n3.vKeys.size(), &lNodes.front()));
                            lNodes.front().lit = lNodes.begin();
                        }
                    }
                    if (n4.vKeys.size() > 0) {
                        lNodes.push_front(n4);
                        if (n4.vKeys.size() > 1) {
                            vSizeAndPointerToNode.push_back(
                              std::make_pair(n4.vKeys.size(), &lNodes.front()));
                            lNodes.front().lit = lNodes.begin();
                        }
                    }

                    lNodes.erase(vPrevSizeAndPointerToNode[j].second->lit);

                    if ((int) lNodes.size() >= N)
                        break;
                }

                if ((int) lNodes.size() >= N || (int) lNodes.size() == prevSize)
                    bFinish = true;
            }
        }
    }

    // Retain the best point in each node
    std::vector<cv::KeyPoint> vResultKeys;
    vResultKeys.reserve(nfeatures);
    for (std::list<ExtractorNode>::iterator lit = lNodes.begin();
         lit != lNodes.end();
         lit++) {
        std::vector<cv::KeyPoint> &vNodeKeys = lit->vKeys;
        cv::KeyPoint *pKP = &vNodeKeys[0];
        float maxResponse = pKP->response;

        for (size_t k = 1; k < vNodeKeys.size(); k++) {
            if (vNodeKeys[k].response > maxResponse) {
                pKP = &vNodeKeys[k];
                maxResponse = vNodeKeys[k].response;
            }
        }

        vResultKeys.push_back(*pKP);
    }

    return vResultKeys;
}

void ORBextractor::ComputeKeyPointsOctTree(
  std::vector<std::vector<cv::KeyPoint> > &allKeypoints) {
    allKeypoints.resize(nlevels);

    const float W = 30;

    for (int level = 0; level < nlevels; ++level) {
        const int minBorderX = EDGE_THRESHOLD - 3;
        const int minBorderY = minBorderX;
        const int maxBorderX = mvImagePyramid[level].cols - EDGE_THRESHOLD + 3;
        const int maxBorderY = mvImagePyramid[level].rows - EDGE_THRESHOLD + 3;

        std::vector<cv::KeyPoint> vToDistributeKeys;
        vToDistributeKeys.reserve(nfeatures * 10);

        const float width = (maxBorderX - minBorderX);
        const float height = (maxBorderY - minBorderY);

        const int nCols = width / W;
        const int nRows = height / W;
        const int wCell = ceil(width / nCols);
        const int hCell = ceil(height / nRows);

        for (int i = 0; i < nRows; i++) {
            const float iniY = minBorderY + i * hCell;
            float maxY = iniY + hCell + 6;

            if (iniY >= maxBorderY - 3)
                continue;
            if (maxY > maxBorderY)
                maxY = maxBorderY;

            for (int j = 0; j < nCols; j++) {
                const float iniX = minBorderX + j * wCell;
                float maxX = iniX + wCell + 6;
                if (iniX >= maxBorderX - 6)
                    continue;
                if (maxX > maxBorderX)
                    maxX = maxBorderX;

                std::vector<cv::KeyPoint> vKeysCell;
                FAST(mvImagePyramid[level]
                       .rowRange(iniY, maxY)
                       .colRange(iniX, maxX),
                     vKeysCell,
                     iniThFAST,
                     true);

                if (vKeysCell.empty()) {
                    FAST(mvImagePyramid[level]
                           .rowRange(iniY, maxY)
                           .colRange(iniX, maxX),
                         vKeysCell,
                         minThFAST,
                         true);
                }

                if (!vKeysCell.empty()) {
                    for (std::vector<cv::KeyPoint>::iterator vit = vKeysCell.begin();
                         vit != vKeysCell.end();
                         vit++) {
                        (*vit).pt.x += j * wCell;
                        (*vit).pt.y += i * hCell;
                        vToDistributeKeys.push_back(*vit);
                    }
                }
            }
        }

        std::vector<cv::KeyPoint> &keypoints = allKeypoints[level];
        keypoints.reserve(nfeatures);

        keypoints = DistributeOctTree(vToDistributeKeys,
                                      minBorderX,
                                      maxBorderX,
                                      minBorderY,
                                      maxBorderY,
                                      mnFeaturesPerLevel[level],
                                      level);

        const int scaledPatchSize = PATCH_SIZE * mvScaleFactor[level];

        // Add border to coordinates and scale information
        const int nkps = keypoints.size();
        for (int i = 0; i < nkps; i++) {
            keypoints[i].pt.x += minBorderX;
            keypoints[i].pt.y += minBorderY;
            keypoints[i].octave = level;
            keypoints[i].size = scaledPatchSize;
        }
    }

    // compute orientations
    for (int level = 0; level < nlevels; ++level)
        computeOrientation(mvImagePyramid[level], allKeypoints[level], umax);
}

void ORBextractor::ComputeKeyPointsOld(
  std::vector<std::vector<cv::KeyPoint> > &allKeypoints) {
    allKeypoints.resize(nlevels);

    float imageRatio = (float) mvImagePyramid[0].cols / mvImagePyramid[0].rows;

    for (int level = 0; level < nlevels; ++level) {
        const int nDesiredFeatures = mnFeaturesPerLevel[level];

        const int levelCols = sqrt((float) nDesiredFeatures / (5 * imageRatio));
        const int levelRows = imageRatio * levelCols;

        const int minBorderX = EDGE_THRESHOLD;
        const int minBorderY = minBorderX;
        const int maxBorderX = mvImagePyramid[level].cols - EDGE_THRESHOLD;
        const int maxBorderY = mvImagePyramid[level].rows - EDGE_THRESHOLD;

        const int W = maxBorderX - minBorderX;
        const int H = maxBorderY - minBorderY;
        const int cellW = ceil((float) W / levelCols);
        const int cellH = ceil((float) H / levelRows);

        const int nCells = levelRows * levelCols;
        const int nfeaturesCell = ceil((float) nDesiredFeatures / nCells);

        std::vector<std::vector<std::vector<cv::KeyPoint> > > cellKeyPoints(
          levelRows, std::vector<std::vector<cv::KeyPoint> >(levelCols));

        std::vector<std::vector<int> > nToRetain(levelRows, std::vector<int>(levelCols, 0));
        std::vector<std::vector<int> > nTotal(levelRows,    std::vector<int>(levelCols, 0));
        std::vector<std::vector<bool> > bNoMore(levelRows,  std::vector<bool>(levelCols, false));
        std::vector<int> iniXCol(levelCols);
        std::vector<int> iniYRow(levelRows);
        int nNoMore = 0;
        int nToDistribute = 0;


        float hY = cellH + 6;

        for (int i = 0; i < levelRows; i++) {
            const float iniY = minBorderY + i * cellH - 3;
            iniYRow[i] = iniY;

            if (i == levelRows - 1) {
                hY = maxBorderY + 3 - iniY;
                if (hY <= 0)
                    continue;
            }

            float hX = cellW + 6;

            for (int j = 0; j < levelCols; j++) {
                float iniX;

                if (i == 0) {
                    iniX = minBorderX + j * cellW - 3;
                    iniXCol[j] = iniX;
                } else {
                    iniX = iniXCol[j];
                }


                if (j == levelCols - 1) {
                    hX = maxBorderX + 3 - iniX;
                    if (hX <= 0)
                        continue;
                }


                cv::Mat cellImage = mvImagePyramid[level]
                                  .rowRange(iniY, iniY + hY)
                                  .colRange(iniX, iniX + hX);

                cellKeyPoints[i][j].reserve(nfeaturesCell * 5);

                FAST(cellImage, cellKeyPoints[i][j], iniThFAST, true);

                if (cellKeyPoints[i][j].size() <= 3) {
                    cellKeyPoints[i][j].clear();

                    FAST(cellImage, cellKeyPoints[i][j], minThFAST, true);
                }


                const int nKeys = cellKeyPoints[i][j].size();
                nTotal[i][j] = nKeys;

                if (nKeys > nfeaturesCell) {
                    nToRetain[i][j] = nfeaturesCell;
                    bNoMore[i][j] = false;
                } else {
                    nToRetain[i][j] = nKeys;
                    nToDistribute += nfeaturesCell - nKeys;
                    bNoMore[i][j] = true;
                    nNoMore++;
                }
            }
        }


        // Retain by score

        while (nToDistribute > 0 && nNoMore < nCells) {
            int nNewFeaturesCell =
              nfeaturesCell + ceil((float) nToDistribute / (nCells - nNoMore));
            nToDistribute = 0;

            for (int i = 0; i < levelRows; i++) {
                for (int j = 0; j < levelCols; j++) {
                    if (!bNoMore[i][j]) {
                        if (nTotal[i][j] > nNewFeaturesCell) {
                            nToRetain[i][j] = nNewFeaturesCell;
                            bNoMore[i][j] = false;
                        } else {
                            nToRetain[i][j] = nTotal[i][j];
                            nToDistribute += nNewFeaturesCell - nTotal[i][j];
                            bNoMore[i][j] = true;
                            nNoMore++;
                        }
                    }
                }
            }
        }

        std::vector<cv::KeyPoint> &keypoints = allKeypoints[level];
        keypoints.reserve(nDesiredFeatures * 2);

        const int scaledPatchSize = PATCH_SIZE * mvScaleFactor[level];

        // Retain by score and transform coordinates
        for (int i = 0; i < levelRows; i++) {
            for (int j = 0; j < levelCols; j++) {
                std::vector<cv::KeyPoint> &keysCell = cellKeyPoints[i][j];
                cv::KeyPointsFilter::retainBest(keysCell, nToRetain[i][j]);
                if ((int) keysCell.size() > nToRetain[i][j])
                    keysCell.resize(nToRetain[i][j]);


                for (size_t k = 0, kend = keysCell.size(); k < kend; k++) {
                    keysCell[k].pt.x += iniXCol[j];
                    keysCell[k].pt.y += iniYRow[i];
                    keysCell[k].octave = level;
                    keysCell[k].size = scaledPatchSize;
                    keypoints.push_back(keysCell[k]);
                }
            }
        }

        if ((int) keypoints.size() > nDesiredFeatures) {
            cv::KeyPointsFilter::retainBest(keypoints, nDesiredFeatures);
            keypoints.resize(nDesiredFeatures);
        }
    }

    // and compute orientations
    for (int level = 0; level < nlevels; ++level)
        computeOrientation(mvImagePyramid[level], allKeypoints[level], umax);
}

static void computeDescriptors(const  cv::Mat &image,
                               std::vector<cv::KeyPoint> &keypoints,
                               cv::Mat &descriptors,
                               const std::vector<cv::Point> &pattern) {
    descriptors = cv::Mat::zeros((int) keypoints.size(), 32, CV_8UC1);

    for (size_t i = 0; i < keypoints.size(); i++)
        computeOrbDescriptor(
          keypoints[i], image, &pattern[0], descriptors.ptr((int) i));
}

void ORBextractor::operator()(cv::InputArray _image,
                              cv::InputArray _mask,
                              std::vector<cv::KeyPoint> &_keypoints,
                              cv::OutputArray _descriptors) {
    if (_image.empty())
        return;

    cv::Mat image = _image.getMat();
    assert(image.type() == CV_8UC1);

    // Pre-compute the scale pyramid
    ComputePyramid(image);

    std::vector<std::vector<cv::KeyPoint> > allKeypoints;
    ComputeKeyPointsOctTree(allKeypoints);
    // ComputeKeyPointsOld(allKeypoints);

    cv::Mat descriptors;

    int nkeypoints = 0;
    for (int level = 0; level < nlevels; ++level)
        nkeypoints += (int) allKeypoints[level].size();
    if (nkeypoints == 0)
        _descriptors.release();
    else {
        _descriptors.create(nkeypoints, 32, CV_8U);
        descriptors = _descriptors.getMat();
    }

    _keypoints.clear();
    _keypoints.reserve(nkeypoints);

    int offset = 0;
    for (int level = 0; level < nlevels; ++level) {
        std::vector<cv::KeyPoint> &keypoints = allKeypoints[level];
        int nkeypointsLevel = (int) keypoints.size();

        if (nkeypointsLevel == 0)
            continue;

        // preprocess the resized image
        cv::Mat workingMat = mvImagePyramid[level].clone();
        cv::GaussianBlur(
          workingMat, workingMat, cv::Size(7, 7), 2, 2, cv::BORDER_REFLECT_101);

        // Compute the descriptors
        cv::Mat desc = descriptors.rowRange(offset, offset + nkeypointsLevel);
        computeDescriptors(workingMat, keypoints, desc, pattern);

        offset += nkeypointsLevel;

        // Scale keypoint coordinates
        if (level != 0) {
            float scale = mvScaleFactor[level];  // getScale(level, firstLevel,
                                                 // scaleFactor);
            for (std::vector<cv::KeyPoint>::iterator keypoint = keypoints.begin(),
                                            keypointEnd = keypoints.end();
                 keypoint != keypointEnd;
                 ++keypoint)
                keypoint->pt *= scale;
        }
        // And add the keypoints to the output
        _keypoints.insert(_keypoints.end(), keypoints.begin(), keypoints.end());
    }
}

void ORBextractor::ComputePyramid(cv::Mat image) {
    for (int level = 0; level < nlevels; ++level) {
        float scale = mvInvScaleFactor[level];
        cv::Size sz(cvRound((float) image.cols * scale),
                cvRound((float) image.rows * scale));
        cv::Size wholeSize(sz.width + EDGE_THRESHOLD * 2,
                       sz.height + EDGE_THRESHOLD * 2);
        cv::Mat temp(wholeSize, image.type()), masktemp;
        mvImagePyramid[level] =
          temp(cv::Rect(EDGE_THRESHOLD, EDGE_THRESHOLD, sz.width, sz.height));

        // Compute the resized image
        if (level != 0) {
            resize(mvImagePyramid[level - 1],
                   mvImagePyramid[level],
                   sz,
                   0,
                   0,
                   cv::INTER_LINEAR);

            copyMakeBorder(mvImagePyramid[level],
                           temp,
                           EDGE_THRESHOLD,
                           EDGE_THRESHOLD,
                           EDGE_THRESHOLD,
                           EDGE_THRESHOLD,
                           cv::BORDER_REFLECT_101 + cv::BORDER_ISOLATED);
        } else {
            copyMakeBorder(image,
                           temp,
                           EDGE_THRESHOLD,
                           EDGE_THRESHOLD,
                           EDGE_THRESHOLD,
                           EDGE_THRESHOLD,
                           cv::BORDER_REFLECT_101);
        }
    }
}
}  // namespace SIVO
